test_distributed_sht.py

# coding=utf-8

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import os
import unittest
from parameterized import parameterized

import torch
import torch.nn.functional as F
import torch.distributed as dist
import torch_harmonics as th
import torch_harmonics.distributed as thd


class TestDistributedSphericalHarmonicTransform(unittest.TestCase):
    """
    Test the distributed spherical harmonic transform module.

    Parameters
    ----------
    nlat : int
        Number of latitude points
    nlon : int
        Number of longitude points
    batch_size : int
        Batch size
    num_chan : int
        Number of channels
    grid : str
        Grid type
    vector : bool
        Whether to use vector spherical harmonic transform
    tol : float
        Tolerance for numerical equivalence
    """

    @classmethod
    def setUpClass(cls):
        # set up distributed
        cls.world_rank = int(os.getenv("WORLD_RANK", 0))
        cls.grid_size_h = int(os.getenv("GRID_H", 1))
        cls.grid_size_w = int(os.getenv("GRID_W", 1))
        port = int(os.getenv("MASTER_PORT", "29501"))
        master_address = os.getenv("MASTER_ADDR", "localhost")
        cls.world_size = cls.grid_size_h * cls.grid_size_w

        if torch.cuda.is_available():
            if cls.world_rank == 0:
                print("Running test on GPU")
            local_rank = cls.world_rank % torch.cuda.device_count()
            cls.device = torch.device(f"cuda:{local_rank}")
            torch.cuda.manual_seed(333)
            proc_backend = "nccl"
        else:
            if cls.world_rank == 0:
                print("Running test on CPU")
            cls.device = torch.device("cpu")
            proc_backend = "gloo"
        torch.manual_seed(333)

        dist.init_process_group(backend=proc_backend, init_method=f"tcp://{master_address}:{port}", rank=cls.world_rank, world_size=cls.world_size)

        cls.wrank = cls.world_rank % cls.grid_size_w
        cls.hrank = cls.world_rank // cls.grid_size_w

        # now set up the comm groups:
        # set default
        cls.w_group = None
        cls.h_group = None

        # do the init
        wgroups = []
        for w in range(0, cls.world_size, cls.grid_size_w):
            start = w
            end = w + cls.grid_size_w
            wgroups.append(list(range(start, end)))

        if cls.world_rank == 0:
            print("w-groups:", wgroups)
        for grp in wgroups:
            if len(grp) == 1:
                continue
            tmp_group = dist.new_group(ranks=grp)
            if cls.world_rank in grp:
                cls.w_group = tmp_group

        # transpose:
        hgroups = [sorted(list(i)) for i in zip(*wgroups)]

        if cls.world_rank == 0:
            print("h-groups:", hgroups)
        for grp in hgroups:
            if len(grp) == 1:
                continue
            tmp_group = dist.new_group(ranks=grp)
            if cls.world_rank in grp:
                cls.h_group = tmp_group

        # set seed
        torch.manual_seed(333)

        if cls.world_rank == 0:
            print(f"Running distributed tests on grid H x W = {cls.grid_size_h} x {cls.grid_size_w}")

        # initializing sht
        thd.init(cls.h_group, cls.w_group)

    @classmethod
    def tearDownClass(cls):
        thd.finalize()
        dist.destroy_process_group(None)

    def _split_helper(self, tensor):
        """
        Split the tensor along the W and H dimensions.

        Parameters
        ----------
        tensor : torch.Tensor
            The tensor to split

        Returns
        -------
        torch.Tensor
            The split tensor
        """
        with torch.no_grad():
            # split in W
            tensor_list_local = thd.split_tensor_along_dim(tensor, dim=-1, num_chunks=self.grid_size_w)
            tensor_local = tensor_list_local[self.wrank]

            # split in H
            tensor_list_local = thd.split_tensor_along_dim(tensor_local, dim=-2, num_chunks=self.grid_size_h)
            tensor_local = tensor_list_local[self.hrank]

        return tensor_local

    def _gather_helper_fwd(self, tensor, B, C, transform_dist, vector):
        """
        Gather the tensor along the W and H dimensions.

        Parameters
        ----------
        tensor : torch.Tensor
            The tensor to gather
        B : int
            Batch size
        C : int
            Number of channels
        transform_dist : thd.DistributedRealSHT or thd.DistributedRealVectorSHT
            The distributed transform
        vector : bool
            Whether to use vector spherical harmonic transform

        Returns
        -------
        torch.Tensor
            The gathered tensor
        """
        # we need the shapes
        l_shapes = transform_dist.l_shapes
        m_shapes = transform_dist.m_shapes

        # gather in W
        if self.grid_size_w > 1:
            if vector:
                gather_shapes = [(B, C, 2, l_shapes[self.hrank], m) for m in m_shapes]
            else:
                gather_shapes = [(B, C, l_shapes[self.hrank], m) for m in m_shapes]
            olist = [torch.empty(shape, dtype=tensor.dtype, device=tensor.device) for shape in gather_shapes]
            olist[self.wrank] = tensor
            dist.all_gather(olist, tensor, group=self.w_group)
            tensor_gather = torch.cat(olist, dim=-1)
        else:
            tensor_gather = tensor

        # gather in H
        if self.grid_size_h > 1:
            if vector:
                gather_shapes = [(B, C, 2, l, transform_dist.mmax) for l in l_shapes]
            else:
                gather_shapes = [(B, C, l, transform_dist.mmax) for l in l_shapes]
            olist = [torch.empty(shape, dtype=tensor_gather.dtype, device=tensor_gather.device) for shape in gather_shapes]
            olist[self.hrank] = tensor_gather
            dist.all_gather(olist, tensor_gather, group=self.h_group)
            tensor_gather = torch.cat(olist, dim=-2)

        return tensor_gather

    def _gather_helper_bwd(self, tensor, B, C, transform_dist, vector):
        """
        Gather the tensor along the W and H dimensions.

        Parameters
        ----------
        tensor : torch.Tensor
            The tensor to gather
        B : int
            Batch size
        C : int
            Number of channels
        transform_dist : thd.DistributedRealSHT or thd.DistributedRealVectorSHT
            The distributed transform
        vector : bool
            Whether to use vector spherical harmonic transform

        Returns
        -------
        torch.Tensor
            The gathered tensor
        """
        
        # we need the shapes
        lat_shapes = transform_dist.lat_shapes
        lon_shapes = transform_dist.lon_shapes

        # gather in W
        if self.grid_size_w > 1:
            if vector:
                gather_shapes = [(B, C, 2, lat_shapes[self.hrank], w) for w in lon_shapes]
            else:
                gather_shapes = [(B, C, lat_shapes[self.hrank], w) for w in lon_shapes]
            olist = [torch.empty(shape, dtype=tensor.dtype, device=tensor.device) for shape in gather_shapes]
            olist[self.wrank] = tensor
            dist.all_gather(olist, tensor, group=self.w_group)
            tensor_gather = torch.cat(olist, dim=-1)
        else:
            tensor_gather = tensor

        # gather in H
        if self.grid_size_h > 1:
            if vector:
                gather_shapes = [(B, C, 2, h, transform_dist.nlon) for h in lat_shapes]
            else:
                gather_shapes = [(B, C, h, transform_dist.nlon) for h in lat_shapes]
            olist = [torch.empty(shape, dtype=tensor_gather.dtype, device=tensor_gather.device) for shape in gather_shapes]
            olist[self.hrank] = tensor_gather
            dist.all_gather(olist, tensor_gather, group=self.h_group)
            tensor_gather = torch.cat(olist, dim=-2)

        return tensor_gather

    @parameterized.expand(
        [
            [256, 512, 32, 8, "equiangular", False, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", False, 1e-9],
            [256, 512, 32, 8, "equiangular", False, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", False, 1e-9],
            [256, 512, 32, 8, "equiangular", False, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", False, 1e-9],
            [361, 720, 1, 10, "equiangular", False, 1e-6],
            [361, 720, 1, 10, "legendre-gauss", False, 1e-6],
            [256, 512, 32, 8, "equiangular", True, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", True, 1e-9],
            [256, 512, 32, 8, "equiangular", True, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", True, 1e-9],
            [256, 512, 32, 8, "equiangular", True, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", True, 1e-9],
            [361, 720, 1, 10, "equiangular", True, 1e-6],
            [361, 720, 1, 10, "legendre-gauss", True, 1e-6],
        ]
    )
    def test_distributed_sht(self, nlat, nlon, batch_size, num_chan, grid, vector, tol):
        """
        Test the distributed spherical harmonic transform.

        Parameters
        ----------
        nlat : int
            Number of latitude points
        nlon : int
            Number of longitude points
        batch_size : int
            Batch size
        num_chan : int
            Number of channels
        grid : str
            Grid type
        vector : bool
            Whether to use vector spherical harmonic transform
        tol : float
            Tolerance for numerical equivalence
        """

        B, C, H, W = batch_size, num_chan, nlat, nlon

        # set up handles
        if vector:
            forward_transform_local = th.RealVectorSHT(nlat=H, nlon=W, grid=grid).to(self.device)
            forward_transform_dist = thd.DistributedRealVectorSHT(nlat=H, nlon=W, grid=grid).to(self.device)
        else:
            forward_transform_local = th.RealSHT(nlat=H, nlon=W, grid=grid).to(self.device)
            forward_transform_dist = thd.DistributedRealSHT(nlat=H, nlon=W, grid=grid).to(self.device)

        # create tensors
        if vector:
            inp_full = torch.randn((B, C, 2, H, W), dtype=torch.float32, device=self.device)
        else:
            inp_full = torch.randn((B, C, H, W), dtype=torch.float32, device=self.device)

        #############################################################
        # local transform
        #############################################################
        # FWD pass
        inp_full.requires_grad = True
        out_full = forward_transform_local(inp_full)

        # create grad for backward
        with torch.no_grad():
            # create full grad
            ograd_full = torch.randn_like(out_full)

        # BWD pass
        out_full.backward(ograd_full)
        igrad_full = inp_full.grad.clone()

        #############################################################
        # distributed transform
        #############################################################
        # FWD pass
        inp_local = self._split_helper(inp_full)
        inp_local.requires_grad = True
        out_local = forward_transform_dist(inp_local)

        # BWD pass
        ograd_local = self._split_helper(ograd_full)
        out_local = forward_transform_dist(inp_local)
        out_local.backward(ograd_local)
        igrad_local = inp_local.grad.clone()

        #############################################################
        # evaluate FWD pass
        #############################################################
        with torch.no_grad():
            out_gather_full = self._gather_helper_fwd(out_local, B, C, forward_transform_dist, vector)
            err = torch.mean(torch.norm(out_full - out_gather_full, p="fro", dim=(-1, -2)) / torch.norm(out_full, p="fro", dim=(-1, -2)))
            if self.world_rank == 0:
                print(f"final relative error of output: {err.item()}")
        self.assertTrue(err.item() <= tol)

        #############################################################
        # evaluate BWD pass
        #############################################################
        with torch.no_grad():
            igrad_gather_full = self._gather_helper_bwd(igrad_local, B, C, forward_transform_dist, vector)
            err = torch.mean(torch.norm(igrad_full - igrad_gather_full, p="fro", dim=(-1, -2)) / torch.norm(igrad_full, p="fro", dim=(-1, -2)))
            if self.world_rank == 0:
                print(f"final relative error of gradients: {err.item()}")
        self.assertTrue(err.item() <= tol)

    @parameterized.expand(
        [
            [256, 512, 32, 8, "equiangular", False, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", False, 1e-9],
            [256, 512, 32, 8, "equiangular", False, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", False, 1e-9],
            [256, 512, 32, 8, "equiangular", False, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", False, 1e-9],
            [361, 720, 1, 10, "equiangular", False, 1e-6],
            [361, 720, 1, 10, "legendre-gauss", False, 1e-6],
            [256, 512, 32, 8, "equiangular", True, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", True, 1e-9],
            [256, 512, 32, 8, "equiangular", True, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", True, 1e-9],
            [256, 512, 32, 8, "equiangular", True, 1e-9],
            [256, 512, 32, 8, "legendre-gauss", True, 1e-9],
            [361, 720, 1, 10, "equiangular", True, 1e-6],
            [361, 720, 1, 10, "legendre-gauss", True, 1e-6],
        ]
    )
    def test_distributed_isht(self, nlat, nlon, batch_size, num_chan, grid, vector, tol):
        """
        Test the distributed inverse spherical harmonic transform.

        Parameters
        ----------
        nlat : int
            Number of latitude points
        nlon : int
            Number of longitude points
        batch_size : int
            Batch size
        num_chan : int
            Number of channels
        grid : str
            Grid type
        vector : bool
            Whether to use vector spherical harmonic transform
        tol : float
            Tolerance for numerical equivalence
        """
        
        B, C, H, W = batch_size, num_chan, nlat, nlon

        if vector:
            forward_transform_local = th.RealVectorSHT(nlat=H, nlon=W, grid=grid).to(self.device)
            backward_transform_local = th.InverseRealVectorSHT(nlat=H, nlon=W, grid=grid).to(self.device)
            backward_transform_dist = thd.DistributedInverseRealVectorSHT(nlat=H, nlon=W, grid=grid).to(self.device)
        else:
            forward_transform_local = th.RealSHT(nlat=H, nlon=W, grid=grid).to(self.device)
            backward_transform_local = th.InverseRealSHT(nlat=H, nlon=W, grid=grid).to(self.device)
            backward_transform_dist = thd.DistributedInverseRealSHT(nlat=H, nlon=W, grid=grid).to(self.device)

        # create tensors
        if vector:
            dummy_full = torch.randn((B, C, 2, H, W), dtype=torch.float32, device=self.device)
        else:
            dummy_full = torch.randn((B, C, H, W), dtype=torch.float32, device=self.device)
        inp_full = forward_transform_local(dummy_full)

        #############################################################
        # local transform
        #############################################################
        # FWD pass
        inp_full.requires_grad = True
        out_full = backward_transform_local(inp_full)

        # create grad for backward
        with torch.no_grad():
            # create full grad
            ograd_full = torch.randn_like(out_full)

        # BWD pass
        out_full.backward(ograd_full)

        # repeat once due to known irfft bug
        inp_full.grad = None
        out_full = backward_transform_local(inp_full)
        out_full.backward(ograd_full)
        igrad_full = inp_full.grad.clone()

        #############################################################
        # distributed transform
        #############################################################
        # FWD pass
        inp_local = self._split_helper(inp_full)
        inp_local.requires_grad = True
        out_local = backward_transform_dist(inp_local)

        # BWD pass
        ograd_local = self._split_helper(ograd_full)
        out_local = backward_transform_dist(inp_local)
        out_local.backward(ograd_local)
        igrad_local = inp_local.grad.clone()

        #############################################################
        # evaluate FWD pass
        #############################################################
        with torch.no_grad():
            out_gather_full = self._gather_helper_bwd(out_local, B, C, backward_transform_dist, vector)
            err = torch.mean(torch.norm(out_full - out_gather_full, p="fro", dim=(-1, -2)) / torch.norm(out_full, p="fro", dim=(-1, -2)))
            if self.world_rank == 0:
                print(f"final relative error of output: {err.item()}")
        self.assertTrue(err.item() <= tol)

        #############################################################
        # evaluate BWD pass
        #############################################################
        with torch.no_grad():
            igrad_gather_full = self._gather_helper_fwd(igrad_local, B, C, backward_transform_dist, vector)
            err = torch.mean(torch.norm(igrad_full - igrad_gather_full, p="fro", dim=(-1, -2)) / torch.norm(igrad_full, p="fro", dim=(-1, -2)))
            if self.world_rank == 0:
                print(f"final relative error of gradients: {err.item()}")
        self.assertTrue(err.item() <= tol)


if __name__ == "__main__":
    unittest.main()